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Stratification of the severity of critically ill patients with classification trees

BACKGROUND: Development of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SA...

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Autores principales: Trujillano, Javier, Badia, Mariona, Serviá, Luis, March, Jaume, Rodriguez-Pozo, Angel
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2797013/
https://www.ncbi.nlm.nih.gov/pubmed/20003229
http://dx.doi.org/10.1186/1471-2288-9-83
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author Trujillano, Javier
Badia, Mariona
Serviá, Luis
March, Jaume
Rodriguez-Pozo, Angel
author_facet Trujillano, Javier
Badia, Mariona
Serviá, Luis
March, Jaume
Rodriguez-Pozo, Angel
author_sort Trujillano, Javier
collection PubMed
description BACKGROUND: Development of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR). METHODS: Retrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%). RESULTS: CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69-75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)). CONCLUSION: With different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients.
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spelling pubmed-27970132009-12-23 Stratification of the severity of critically ill patients with classification trees Trujillano, Javier Badia, Mariona Serviá, Luis March, Jaume Rodriguez-Pozo, Angel BMC Med Res Methodol Research Article BACKGROUND: Development of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR). METHODS: Retrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%). RESULTS: CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69-75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)). CONCLUSION: With different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients. BioMed Central 2009-12-09 /pmc/articles/PMC2797013/ /pubmed/20003229 http://dx.doi.org/10.1186/1471-2288-9-83 Text en Copyright ©2009 Trujillano et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Trujillano, Javier
Badia, Mariona
Serviá, Luis
March, Jaume
Rodriguez-Pozo, Angel
Stratification of the severity of critically ill patients with classification trees
title Stratification of the severity of critically ill patients with classification trees
title_full Stratification of the severity of critically ill patients with classification trees
title_fullStr Stratification of the severity of critically ill patients with classification trees
title_full_unstemmed Stratification of the severity of critically ill patients with classification trees
title_short Stratification of the severity of critically ill patients with classification trees
title_sort stratification of the severity of critically ill patients with classification trees
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2797013/
https://www.ncbi.nlm.nih.gov/pubmed/20003229
http://dx.doi.org/10.1186/1471-2288-9-83
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